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Ukiyo-e Analysis and Creativity with Attribute and Geometry Annotation 12

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Ukiyo-e Analysis and Creativity with Attribute and Geometry ...

12th International Conference on Computational Creativity, ICCC 2021

作者： Tian, Yingtao Clanuwat, Tarin Suzuki, Chikahiko Kitamoto, Asanobu Google Brain Tokyo Japan ROIS-DS Center for Open Data in the Humanities NII Japan

ISBN: (纸本)9789895416035

The study of Ukiyo-e, an important genre of pre-modern Japanese art, focuses on the object and style like other artwork researches. Such study has benefited from the renewed interest by the machine learning community in culturally important topics, leading to interdisciplinary works including collections of images, quantitative approaches, and machine learning-based creativities. They, however, have several drawbacks, and it remains challenging to integrate these works into a comprehensive view. To bridge this gap, we propose a holistic approach: We first present a large-scale Ukiyo-e dataset with coherent semantic labels and geometric annotations, then show its value in a quantitative study of Ukiyo-e paintings’ object using these labels and annotations. We further demonstrate the machine learning methods could help style study through soft color decomposition of Ukiyo-e, and finally provides joint insights into object and style by composing sketches and colors using colorization. We make our dataset (Tian, CODH, and ARC 2021) available online 1 © ICCC *** rights reserved.

关键词： Semantics

Recognizing modern Japanese magazines by combining Deep Learning with language models 13

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Recognizing modern Japanese magazines by combining Deep Lear...

13th International Conference Knowledge and Systems Engineering, KSE 2021

作者： Nguyen, Nhan Thien Nguyen, Dang Minh Le, Anh Duc Quan, Tho Thanh University of Technology Vietnam National University Hcmc Faculty of Computer Science and Engineering Ho Chi Minh Viet Nam Center for Open Data in the Humanities Tokyo Japan

ISBN: (纸本)9781665499750

As one of the most culturally rich countries globally, Japan also has a rich history of magazines. In modern Japanese magazines, which were published during the 19-20th centuries, Japanese usage is similar to the current style of the contemporary Japanese language. However, most of those documents are not digitized but stored as images. Due to their importance to Japanese culture, history, and other socio-scientific topics, the problem of using computers to help identify these image-based modern magazines has been investigated from research and widely disseminated through the use of different methods in Deep Learning. However, these methods are still limited to achieve strong performance in recognizing handwriting images, especially uncommon Kanji characters. In this research, we address this problem by developing a deep learning-based language model and integrating it into the current OCR system for modern Japanese magazine documents. We also propose a combination strategy between the current Japanese OCR tool and our language model. The strategy will learn where the system should rely on OCR (e.g., Hiragana and Common kanji characters recognized correctly by OCR) or language model (uncommon Kanji characters are frequently recognized incorrectly by OCR, the system should rely on the language model). Our method enjoys visible improvement once experimented with real data. © 2021 IEEE.

关键词： Optical character recognition

Ukiyo-e analysis and creativity with attribute and geometry annotation

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arXiv 2021年

作者： Tian, Yingtao Clanuwat, Tarin Suzuki, Chikahiko Kitamoto, Asanobu Google Brain Tokyo Japan ROIS-DS Center for Open Data in the Humanities NII

The study of Ukiyo-e, an important genre of pre-modern Japanese art, focuses on the object and style like other artwork researches. Such study has benefited from the renewed interest by the machine learning community in culturally important topics, leading to interdisciplinary works including collections of images, quantitative approaches, and machine learning-based creativities. They, however, have several drawbacks, and it remains challenging to integrate these works into a comprehensive view. To bridge this gap, we propose a holistic approach1: We first present a large-scale Ukiyo-e dataset with coherent semantic labels and geometric annotations, then show its value in a quantitative study of Ukiyo-e paintings' object using these labels and annotations. We further demonstrate the machine learning methods could help style study through soft color decomposition of Ukiyoe, and finally provides joint insights into object and style by composing sketches and colors using colorization. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

An End-to-End Recognition System for Unconstrained Vietnamese Handwriting

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SN Computer Science 2020年第1期1卷 1-8页

作者： Le, Anh Duc Nguyen, Hung Tuan Nakagawa, Masaki Center for Open Data in the Humanities Tokyo Japan Department of Computer and Information Sciences Tokyo University of Agriculture and Technology Tokyo Japan

Inspired by recent successes in neural machine translation and image caption generation, we present an attention-based encoder–decoder model (AED) to recognize handwritten Vietnamese text. The model consists of two parts: a Convolutional Neural Network-based (CNN) encoder and a Long Short-Term Memory-based (LSTM) decoder. The encoder is based on DenseNet for extracting invariant features. The LSTM-based decoder with an attention model incorporated generates output text. The input of the encoder is a handwritten text image and the target of the decoder is the corresponding text of the input image. Our model is trained end-to-end to predict the text from a given input image since all the parts are differential components. In the experiment section, we evaluate our proposed AED model on the VNOnDB-Word and VNOnDB-Line datasets to verify its efficiency. The experiential results show that our model achieves 4.10% of character error eate (CER) and 10.24% of word error rate (WER) on the testing set of VNOnDB-Word and 4.67% of CER and 13.33% of WER on the testing set of VNOnDB-Line without using any language model. This result is higher than our previous system, Nguyen et al.’s system, and the system provided by Google in the Vietnamese Online Handwritten Text Recognition competition. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Attention model CNN Encoder–decoder LSTM Unconstrained Vietnamese handwriting Vietnamese handwriting recognition

KuroNet: Regularized Residual U-Nets for End-to-End Kuzushiji Character Recognition

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SN Computer Science 2020年第3期1卷 1-15页

作者： Lamb, Alex Clanuwat, Tarin Kitamoto, Asanobu Mila Université de Montréal Quebec Canada ROIS-DS Center for Open Data in the Humanities National Institute of Informatics Tokyo Japan

Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the eighth century. Over 3 million books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts. Thus there has been a great deal of interest in using machine learning to automatically recognize these historical texts and transcribe them into modern Japanese characters. Our proposed model KuroNet (which builds on Clanuwat et al. in International conference on document analysis and recognition (ICDAR), 2019) outperforms other model for Kuzushiji recognition. In this paper, KuroNet achieves higher accuracy while still recognizing entire pages of text using the residual U-Net architecture from adding more regularization. We also explore areas where our system is limited and suggests directions for future work. © 2020, The Author(s).

关键词： Character recognition Historical document Japan Kuzushiji Machine learning

Predicting the ordering of characters in Japanese historical documents

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arXiv 2021年

作者： Lamb, Alex Clanuwat, Tarin Han, Siyu Bober-Irizar, Mikel Kitamoto, Asanobu ROIS-DS Center for Open Data in the Humanities National Institute of Informatics German Research Center for Environmental Health University of Cambridge

Japan is a unique country with a distinct cultural heritage, which is reflected in billions of historical documents that have been preserved. However, the change in Japanese writing system in 1900 made these documents inaccessible for the general public. A major research project has been to make these historical documents accessible and understandable. An increasing amount of research has focused on the character recognition task and the location of characters on image, yet less research has focused on how to predict the sequential ordering of the characters. This is because sequence in classical Japanese is very different from modern Japanese. Ordering characters into a sequence is important for making the document text easily readable and searchable. Additionally, it is a necessary step for any kind of natural language processing on the data (e.g. machine translation, language modeling, and word embeddings). We explore a few approaches to the task of predicting the sequential ordering of the characters: one using simple hand-crafted rules, another using hand-crafted rules with adaptive thresholds, and another using a deep recurrent sequence model trained with teacher forcing. We provide a quantitative and qualitative comparison of these techniques as well as their distinct trade-offs. Our best-performing system has an accuracy of 98.65% and has a perfect accuracy on 49% of the books in our dataset, suggesting that the technique is able to predict the order of the characters well enough for many tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

Automated Transcription for Pre-Modern Japanese Kuzushiji Documents by Random Lines Erasure and Curriculum Learning

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arXiv 2020年

作者： Le, Anh Duc Center for Open Data in The Humanities Tokyo Japan

Recognizing the full-page of Japanese historical documents is a challenging problem due to the complex layout/background and difficulty of writing styles, such as cursive and connected characters. Most of the previous methods divided the recognition process into character segmentation and recognition. However, those methods provide only character bounding boxes and classes without text transcription. In this paper, we enlarge our previous human-inspired recognition system from multiple lines to the full-page of Kuzushiji documents. The human-inspired recognition system simulates human eye movement during the reading process. For the lack of training data, we propose a random text line erasure approach that randomly erases text lines and distorts documents. For the convergence problem of the recognition system for full-page documents, we employ curriculum learning that trains the recognition system step by step from the easy level (several text lines of documents) to the difficult level (full-page documents). We tested the step training approach and random text line erasure approach on the dataset of the Kuzushiji recognition competition on Kaggle. The results of the experiments demonstrate the effectiveness of our proposed approaches. These results are competitive with other participants of the Kuzushiji recognition competition. Copyright © 2020, The Authors. All rights reserved.

关键词： Curricula

KuroNet: Pre-modern japanese kuzushiji character recognition with deep learning 15

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KuroNet: Pre-modern japanese kuzushiji character recognition...

15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Clanuwat, Tarin Lamb, Alex Kitamoto, Asanobu Center for Open Data in the Humanities National Institute of Informatics Tokyo Japan MILA Universite de Montreal Montreal Canada

ISBN: (纸本)9781728128610

Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the 8th century. Over 3 millions books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts. Thus there has been a great deal of interest in using Machine Learning to automatically recognize these historical texts and transcribe them into modern Japanese characters. Nevertheless, several challenges in Kuzushiji recognition have made the performance of existing systems extremely poor. To tackle these challenges, we propose KuroNet, a new end-to-end model which jointly recognizes an entire page of text by using a residual U-Net architecture which predicts the location and identity of all characters given a page of text (without any pre-processing). This allows the model to handle long range context, large vocabularies, and non-standardized character layouts. We demonstrate that our system is able to successfully recognize a large fraction of pre-modern Japanese documents, but also explore areas where our system is limited and suggest directions for future work. © 2019 IEEE.

关键词： Character recognition

KaoKore: A Pre-modern Japanese Art Facial Expression dataset

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arXiv 2020年

作者： Tian, Yingtao Suzuki, Chikahiko Clanuwat, Tarin Bober-Irizar, Mikel Lamb, Alex Kitamoto, Asanobu University of Cambridge MILA Université de Montréal Google Brain Tokyo Japan ROIS-DS Center for Open Data in Humanities NII

From classifying handwritten digits to generating strings of text, the datasets which have received long-time focus from the machine learning community vary greatly in their subject matter. This has motivated a renewed interest in building datasets which are socially and culturally relevant, so that algorithmic research may have a more direct and immediate impact on society. One such area is in history and the humanities, where better and relevant machine learning models can accelerate research across various fields. To this end, newly released benchmarks [3] and models [4] have been proposed for transcribing historical Japanese cursive writing, yet for the field as a whole using machine learning for historical Japanese artworks still remains largely uncharted. To bridge this gap, in this work we propose a new dataset KaoKore 1 which consists of faces extracted from pre-modern Japanese artwork. We demonstrate its value as both a dataset for image classification as well as a creative and artistic dataset, which we explore using generative models. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning